MRPL13 is a metastatic and prognostic marker of breast cancer: a silico analysis accompanied with experimental validation

doi:10.21203/rs.3.rs-4325352/v1

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MRPL13 is a metastatic and prognostic marker of breast cancer: a silico analysis accompanied with experimental validation

https://doi.org/10.21203/rs.3.rs-4325352/v1

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Background: Although progress has been made in accurate diagnosis and targeted treatments, breast cancer (BC) patients with metastasis still present a grim prognosis. With the continuous emergence and development of new personalized and precision medicine targeting specific tumor biomarkers, there is an urgent need to find new metastatic and prognostic biomarkers for BC patients.

Methods: We commit to identify genes that associate with metastasis and prognosis in BC by a silico analysis accompanied with experimental validation.

Results: A total of 25 overlap differentially expressed genes were identified. Ten hub genes (namely MRPL13, CTR9, TCEB1, RPLP0, TIMM8B, METTL1, GOLT1B, PLK2, PARL and MANBA) were identified and confirmed. MRPL13, TCEB1, GOLT1Bwere shown be associated with the worse over survival (OS) and were optionally chosen for further verification by western blot. Only MRPL13 was found associated with cells invasion, and the expression ofMRPL13 in metastatic BC was significant higher than in primary BC.

Conclusion: We proposed MRPL13 could be a potential novel biomarkerfor the metastasis and prognosis of breast cancer.

MRPL13

breast cancer

biomarker

metastatic

silico analysis

Breast cancer (BC) is an unusually heterogeneous malignancy with a complex pathogenesis in women [1]. In worldwide, nearly 2.3 million of BCs were diagnosed by 2020. It accounts for 11.7 percent of new cancer cases in women [2]. The mean survival time of BC patients is approximately 35 months and main associated with variables including tumor size and metastasis [3, 4]. Although there have been advances in targeted therapies, the prognosis and quality of life for patients with metastatic BC remain poor [5]. With the continuous emergence and development of new personalized and precision medicine targeting specific tumor biomarkers, more available and reliable tumor biomarkers are urgent and crucial to be identified [6].

The microarray technology has been widely applied to identify novel biomarkers for diagnosis, therapy, and prognosis for tumors [7, 8]. Bioinformatic analysis is also widely used to explain differences in gene expression, helping us better understand the mechanisms of breast cancer occurrence and development [9].

In present, we first downloaded both primary breast cancer and metastases tissue samples datasets (GSE43837, GSE100534 and GSE125989) from Gene Expression Omnibus (GEO) database. Second, gene expression profiles were analyzed to identify differentially expressed genes (DEGs). Thirdly, we performed gene ontology (GO) analysis, Kyoto Encyclopedia of Genes Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) analysis of DEGs. Finally, we verified the significant hub genes in cell lines and clinic tissues. We propose to explore metastatic and prognostic biomarkers, and provide candidate targets for the treatment of BC.

Ethics approval statement

The study was carried out in accordance with the principles of the Declaration of Helsinki, and was approved by Hunan Provincial People's Hospital Review Committee (Number 078). All participants in the study received written informed consent (a blank copy of the human participant information consent was provided: Supplemental File 1 and Supplemental File 2) .

Study design

The design of this study is presented in the form of a flow chart (Fig. 1). First, the differentially expressed genes (DEGs) between the primary breast cancer (BC) samples and metastases BC samples from three cohort profile datasets were screened. Then, the DEGs were integrated by Venn diagram. The protein-protein interaction (PPI) network was constructed basing on the integrated DEGs, and the top ten DEGs were identified as hub genes. The expressions of hub genes and survival analysis were carried out in TCGA dataset using the online software UALCAN, and the significant genes were identified as key genes. Finally, we performed the experiment validation in cell lines and clinic BC tissues.

Samples collection and processing

Human breast cancer tissue specimens were collected from the First Affiliated Hospital of Hunan Normal University (Hunan Provincial People's Hospital). The tissues (n = 60) including 30 primary breast cancer and 30 metastatic breast cancer were immersed in 10% neutral formalin and frozen at -80℃ before processing.

Bioinformatic data source and analysis

We downloaded three gene expression datasets (GSE43837, GSE100534 and GSE125989) from GEO database (http://www.ncbi.nlm.nih.gov/geo). GSE43837 was based on GPL1352-9802 platform (Affymetrix Human X3P Array) and contained 19 primary breast cancer samples and 19 metastases samples. GSE100534 was based on GPL6244-17930 platform (Affymetrix Human Gene 1.0 ST Array) and contained 32 primary breast cancer samples and 3 metastases samples. GSE125989 was based on GPL571-17391 platform (Affymetrix Human Genome U133A 2.0 Array) and contained 16 primary breast cancer samples and 16 metastases samples.

The raw data were downloaded and standardized through the R language affy package [10]. We replaced each probe ID of the expression matrix with the corresponding gene symbol according to the annotation files. Limma R package was used to screen the genes of each dataset and genes with an adjusted P-value < 0.05 and |log2fold change (FC)| >1 were considered as differentially expressed genes (DEGs) [11]. Use the Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/) integrate the three data sets of DEGs and save the consolidated DEGs list for later analysis. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the interaction between DEGs were performed using Search Tool for Retrieval of Interacting Genes/Proteins database (STRING; https://string-db.org/). FunRich were performed to classify functional enrichment of DEGs, including biological process, molecular function, and cellular component terms [12]. Cytoscape software was used to construct the protein-protein interaction (PPI) network of integrated genes, and the top 10 genes were identified as hub genes by the plug-in cytoHubba.

To compare the expression of hub genes in different metastatic status of breast cancer, an interactive web portal to analyze The Cancer Genome Atlas (TCGA) gene expression data deeply, named the UALCAN (http://ualcan.path.uab.edu/) was used [13]. To explore the prognostic value of these hub genes, we investigated the relationship between their DEGs expression and the overall survival (OS) of BC patients with TCGA clinical data by using the ‘‘GDCRNA Tools’’ and ‘‘Survival’’ package in R language.

Cell culture and treatments

To investigate whether there were discrepancies in MRPL13, TCEB1 and GOLT1B expression among different BC cell lines and normal breast cell line, the MDA-MB-231, MCF-7 and Hs 578Bst were derived from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and identified by mycoplasma detection, cell viability assay and DNA fingerprinting. The cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM, Gibco^TMLife Technologies, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco^TMLife Technologies, USA) at 37℃ in 5% CO₂. Then, western blotting was carried out to analyze the expression of MRPL13, TCEB1 and GOLT1B in each cell line.

To knockdown MRPL13, 1×10⁶ cells were incubated with psi-U6-MRPL13 shRNA1#, psi-U6-MRPL13 shRNA2# or psi-U6-MRPL13 shRNA3#, (GeneCopoeia, Rockville, MD) using Lipofactamine3000 (Invitrogen, San Diego, CA) based on the protocol described previously [14], the target sequences were ACCTGAAGATTATCGGCTA, ATCTATAAGACTTCAGGGA and GTCTAGATGAGTACACACA respectively. For the contral, cells were transfected with psi-U6 plasmids (GeneCopoeia, Rockville, MD).

Western blot assay

Cells were lysed using cold RIPA buffer (Beyotime, Wuhan, China) supplemented with protease inhibitors, and protein concentration was determined using Pierce®BCA Protein Assay Kit (Thermo SCIENTIFIC, USA). Equal amounts of protein were separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE). The target protein was transferred onto PVDF membranes according to the molecular weight markers (Thermo SCIENTIFIC, USA) and blocked in buffer [5% (wt/vol) milk, TBST (TBS, pH 7.4, 0.1% Tween-20), 66 rpm, 1 hour, room temperature]. Primary antibodies were diluted in TBST: anti-MRPL13 (1:1000, ab190232; Abcam), anti-TCEB1 (1:1000, ab226831; Abcam), anti-GOLT1B (1:1000, PA5-68055; ThermoFisher) and anti-beta actin (1:1000, ab8227; Abcam), incubated at 4℃ overnight. Then, the membranes were incubated with Goat Anti-Rabbit IgG H&L (HRP) secondary antibodies (1:5000, ab205718; Abcam) for 1 hour at 37℃. Blots were then visualized by using Clarity™ Western ECL Blotting Substrate (Bio-Rad, USA) and imaged in Image Lab system (Bio-Rad, USA).

Wound healing scratch assay

Cell lines MDA-MB-231, MCF-7 and Hs 578Bst were seeded in six-well plates (2×10⁶ cells/well) and grown for 24 hours. After cells attached, scratching was done with a 200 µl sterile pipette tip in each well. Wells were washed using phosphate-buffered saline (PBS), and then cultured for a further 12 and 24 hours with DMEM supplemented with 10% FBS. The migration of the cells to the wound was observed via the inverted microscope (ZEISS, Germany) within the time course.

Transwell invasion assay

To explore whether cell invasion could be affected when MRPL13 knockdown, transwell assays were carried out using a 24-well transwell chamber (Corning Corporation, Corning, NY, USA). Cells were injected into the upper chamber at a density of 1×10⁶ (pore size 8µm) and precoated with Matrigel (BD Biosciences, Franklin Lakes, NJ, USA). The lower chamber was filled with 500µl DMEM containing 10% FBS. After 24 hours of incubation at 37℃, 5% CO2, cells on the upper part of the membrane were removed with a sterile swab, and cells on the lower part of the membrane were observed and counted after crystal violet staining under an inverted microscope (ZEISS, Germany). Five fields were randomly selected and the number of infected cells was counted strictly according to the protocol described previously [15].

Immunohistochemistry (IHC)

IHC for MRPL13 was carried out on a 5µm thick whole-mount sections of formalin-fixed, paraffin-embedded tissue. Antigen retrieval was performed in Ventana Cell Conditioner 1 (Tris-Borate-EDTA) for 58 min. The 3% hydrogen peroxide was used to block the endogenous peroxidase activity for 6 min, and Dulbecco’s phosphate buffered saline (PBS; Life Technologies) with 1% non-fat dry milk, 1% BSA and 0.05% Tween-20 added, was used to block the non-specific protein interactions. Anti-MRPL13 was diluted 1:100 using Dulbecco’s PBS. After washed by distilled water, slides were counterstained with hematoxylin, then dehydrated and mounted using permanent media. Negative controls did not show any signal after incubated with Dulbecco’s PBS instead of primary antibody. IHC staining was evaluated by a blinded experienced pathologist. The staining intensity × percentage of positive cells rule was used to calculate IHC score and four-level system also applied (negative = 0, weak = 1–50, moderate = 51–100 and strong = 101–200). IHC was scored independently by three pathologists using the 2018 ASCO/CAP clinical practice guideline for breast cancer [16]. Inter-observer discrepancies in scores were resolved by consensus.

Statistical analysis

The experimental data were analyzed using Graphpad Prism version 7.0 (GraphPad Software, La Jolla, CA, USA) and the results were presented in form of mean ± standard deviation (SD). Comparison between the two sets of data was performed using Student's t test. All experiments were performed repeatedly for three times and P < 0.05 was considered statistically significant.

Identification of differentially expressed genes (DEGs) between primary and metastases breast cancer

An overlap of 25 DEGs were identified from the three profile data sets, as displayed by Venn diagram (Fig. 2A), all of them were up-regulated. The GSE43837 dataset contained 1341 differential genes, GSE125989 dataset contained 3000 differential genes and GSE100534 dataset contained 898 differential genes. PPI network of DEGs also was constructed and consisted of 20 nodes and 29 edges (Fig. 2B) and ten hub genes were identified by the criterion described above which were MRPL13, CTR9, TCEB1, RPLP0, TIMM8B, METTL1, GOLT1B, PLK2, PARL and MANBA, and these ten hub genes were all up-regulated.

Functional enrichment analysis of differentially expressed genes (DEGs)

The DEGs were mainly enriched in “Nucleus” and “Cytoplasm” by cellular component (Fig. 3A), “Cell communication” and “Signal transduction” by biological process (Fig. 3B), and “Transcription regulator activity” by molecular function (Fig. 3C), respectively. Moreover, differentially expressed genes (DEGs) pathways were enriched and the top one pathways were “Gene expression” (Fig. 3D).

Validation of the hub genes

A total of 1189 samples derived from the TCGA project were used to validate the hub gene transcript expression by UALCAN. The samples included 114 normal samples, 516 primary breast cancer samples and 559 metastasis breast cancer samples that have different metastasis status, so expression values of each hub gene in breast cancer were compared based on metastasis status. Most of the hub genes in breast cancer samples were significantly up-regulated compared with normal samples (P < 0.05, Table 1 and Figure S1), particularly the expression of MRPL13, TCEB1, GOLT1B, TIMM8B and METTL1 (Fig. 4), revealing that these hub genes might be positively correlated with tumor progression.

Table 1

Comparison of expression statistics of hub genes in BC based on nodal metastasis status
Comparison	Normal vs N0	Normal vs N1	Normal vs N2	Normal vs N3	N0 vs N1	N0 vs N2	N0 vs N3	N1 vs N2	N1 vs N3	N2 vs N3
MRPL13	1.62x10^− 12	1.62x10^− 12	4.38x10^− 7	2.96x10^− 12	9.77x10^− 1	4.72x10^− 2	8.68x10^− 3	4.78x10^− 2	8.9x10^− 1	5.15x10^− 2
TCEB1	1.62x10^− 12	1.62x10^− 12	1.62x10^− 12	9.55x10^− 10	8.48x10^− 1	9.08x10^− 3	4.14x10^− 2	1.43x10^− 2	3.71x10^− 1	1.47x10^− 2
GOLT1B	5.50x10^− 13	6.57x10^− 8	2.74x10^− 6	9.63x10^− 3	6.35x10^− 1	3.58x10^− 1	3.22x10^− 2	6.79x10^− 1	3.30x10^− 2	2.59x10^− 2
TIMM8B	1x10^− 12	1.62x10^− 12	3.74x10^− 7	1.01x10^− 6	5.76x10^− 1	9.65x10^− 1	8.53x10^− 1	7.93x10^− 1	6.19x10^− 3	8.69x10^− 3
METTL1	1x10^− 12	1.62x10^− 12	1.62x10^− 12	4.10x10^− 3	8.99x10^− 2	3.91x10^− 2	4.39x10^− 1	6.55x10^− 2	4.54x10^− 2	8.05x10^− 1
CTR9	8.95x10^− 1	3.76x10^− 2	1.27x10^− 1	1.64x10^− 1	1.71x10^− 2	1.08x10^− 1	2.08x10^− 1	6.24x10^− 1	6.94x10^− 2	2.68x10^− 2
RPLP0	8.37x10^− 1	8.93x10^− 2	7.34x10^− 2	3.42x10^− 1	1.86x10^− 2	3.02x10^− 2	3.28x10^− 1	6.72x10^− 1	6.94x10^− 1	5.26x10^− 1
PLK2	1.34x10^− 1	3.67x10^− 3	4.54x10^− 1	6.30x10^− 2	7.92x10^− 2	7.62x10^− 1	3.92x10^− 1	2.05x10^− 1	9.13x10^− 1	3.74x10^− 1
PARL	7.07x10^− 1	9.44x10^− 1	5.69x10^− 2	6.73x10^− 3	7.01x10^− 1	7.28x10^− 1	5.39x10^− 1	5.75x10^− 1	7.18x10^− 1	4.62x10^− 1
MANBA	3.78x10^− 1	5.83x10^− 2	2.06x10^− 2	4.62x10^− 2	6.58x10^− 1	6.34x10^− 2	9.56x10^− 1	2.96x10^− 2	7.62x10^− 1	2.23x10^− 1
BC, breastcancer; Normal, normal breast samples; N0, no regional lymph node metastasis samples; N1, metastases in 1 or 3 axillary lymph nodes samples; N2, metastases in 4 or 9 axillary lymph nodes samples; N3, metastases in 10 or more axillary lymph nodes samples.Numbers in black indicate statistical significance.

The expression of MRPL13, TCEB1, TIMM8B, METTL1 and GOLT1B in breast cancer samples were significantly up-regulated compared with normal samples. Normal, normal breast samples; N0, no regional lymph node metastasis samples; N1, metastases in 1 or 3 axillary lymph nodes samples; N2, metastases in 4 or 9 axillary lymph nodes samples; N3, metastases in 10 or more axillary lymph nodes samples.

Prognostic significance analysis

To further elucidate whether these hub genes affect the survival of breast cancer patients, the overall survival (OS) for each hub gene was analyzed using UALCAN. High expression of MRPL13, TCEB1 and GOLT1B (P = 0.00016, P = 0.041 and P = 0.013 respectively, Fig. 5) were showed associated with the worse OS in breast cancer patients, while the OS differences of the other hub genes were not significant (shown in supplementary Figure S2, P > 0.05), revealing that MRPL13, TCEB1 and GOLT1B could be used as a tumor prognostic predictor for BC patients.

Expression of MRPL13, TCEB1 and GOLT1B in breast cancer cell lines and normal breast cell line

Our western blot date showed that no statistically significant differences in TCEB1 and GOLT1B expression at protein level among our cell lines (Fig. 6A and 6B), while only observed MRPL13 expression was the highest in MDA-MB-231 compared with Hs 578Bst, and the discrepancy was statistically significant (P < 0.05).

Wound healing ability of cell lines

As shown in Fig. 7A and 7B, the healing rate in MDA-MB-231 was much more higher than in MCF7 and Hs 578Bst both at 12 hours and 24 hours ( P < 0.05, P < 0.01), indicating that MDA-MB-231 was the most invasiveness breast cancer cell line. Earlier we also found that highest MRPL13 expression in MDA-MB-231, so we speculated that high MRPL13 expression level might be one of the important promoters for breast cancer cells invasion.

Invasion of MDA-MB-231 and MCF7 with MRPL13 knockdown

To validate our hypothesis, we knockdown the MRPL13 in MDA-MB-231 and MCF7 cells using shRNA1#, shRNA2# and shRNA3#, and the effect was verified by western blotting, all of our designed shRNA could suppress the MRPL13 expression effectively (shown in Fig. 8A). Transwell assay was performed to detect the invasion of MDA-MB-231 (highly invasive breast cells) and MCF7 (in situ breast cancer cells) in the meantime. Compared to the MCF-7, the invasion of MRPL13 knockdown cells were dramatic declined in MDA-MB-231 (Fig. 8B). Our study suggests that silencing MRPL13 expression in highly invasive breast cancer is more effective in inhibiting tumor invasion and metastasis.

Immunohistochemical for MRPL13 in clinic breast cancer tissues

We first establish an immunohistochemical (IHC) scoring criteria for MRPL13 in breast cancer according to the 2018 ASCO/CAP clinical practice guideline for breast cancer [16]. The staining intensity percentage of positive cells rule was used to calculate IHC score and four-level system also applied (negative = 0, weak = 1–50, moderate = 51–100 and strong = 101–200). As shown in Fig. 9, the IHC score of MRPL13 in metastatic BC was significant higher than in primary BC (P < 0.001).

In the present study, the hub genes and multiple molecular pathways in breast cancer (BC) have been identified and verified using silico analysis accompanied with experiment for the first time. An overlap of 25 up-regulated DEGs and ten hub genes, namely MRPL13, CTR9, TCEB1, RPLP0, TIMM8B, METTL1, GOLT1B, PLK2, PARL and MANBA were identified. These genes were mainly enriched in “cell communication, signal transduction”, which were closely related to cancer metastasis. MRPL13, TCEB1, TIMM8B, METTL1 and GOLT1B were associated with tumor progression positively, and MRPL13, TCEB1 and GOLT1B were also shown related to the prognosis of BC patients. High MRPL13 expression promotes cells invasion has been verified in vitro, and the higher expression of MRPL13 in metastatic BC tissues was also found. These all findings provide convincing evidence that MRPL13 could be a metastatic and prognostic biomarker in BC.

MRPL13 (mitochondrial ribosomal protein L13), also known as L13 or L13A, are encoded by nuclear genes and help in protein synthesis within the mitochondrion. Mature ribosomal protein L13a has 202 amino acids (the NH2-terminal methionine is removed after translation of the mRNA) [17]. Transcription of MRPL13 in Saccharomyces cerevisiae was found to be repressed in the presence of glucose and affected cellular growth on non-fermentable carbon sources [18]. MRPL13 was also revealed playing role in adaptation of the translation system to the specific requirements of the organelle [19]. Lee et al found that reduced MRPL13 expression is a key factor in mitoribosome regulation and subsequent oxidative phosphorylation defection which can regulate hepatoma cell invasion activity [20]. The MRPL13 is also critical for the structural and functional integrity of the mitochondrion in Plasmodium falciparum [21]. In addition, a study reported that phosphorylation of ribosomal protein L13a is essential for translational repression of inflammatory genes by the interferon (IFN)-gamma-activated inhibitor of translation (GAIT) complex [22]. Furthermore, high MRPL13 expression showed worse survival [23] and could be a potential prognostic biomarker and novel therapeutic target of breast cancer [24]. Yang et al found MRPL13 could be designed as biomarkers and therapeutic targets for breast cancer [6], and Cai et al verified MRPL13 promotes breast cancer invasion and metastasis through the PI3K/AKT/mTOR signaling pathway [25]. However, in our research, we also revealed in the databases (GSE43837, GSE100534 and GSE125989) that MRPL13 plays an important role in the invasion and metastasis of breast cancer, and further demonstrated that MRPL13 expression is closely related to breast cancer survival and prognosis. This is consistent with the results of previous studies.

The difference with these studies is that they obtained the information from the TCGA database through bioinformatics, and focused on normal subjects and breast cancer patients, and only focused on the function of MRPL13 in breast cancer. Our focus on primary BC and metastatic BC, and we use protein chips to screen the differentially expressed proteins of primary BC and metastatic BC. Results Multiple candidate proteins with differential expression were identified, such as MRPL13, CTR9, TCEB1, RPLP0, TIMM8B, METTL1, GOLT1B, PLK2, PARL and MANBA, providing multiple target proteins for screening and treatment of metastatic BC. By further analyzing the differentially expressed proteins in primary BC and metastatic BC, the results showed that MRPL13 had the highest score. Therefore, further attention is focused on the function of MRPL13 at the cellular level as well as primary BC and metastatic BC. In our study, we knocked down MRPL13 in MDA-MB-231 (highly metastatic malignant breast cancer cell line) and MCF-7 (In situ breast cancer cell line), and the results showed that in h MDA-MB-231, MRPL13 knocked down can significantly inhibit cell invasion compared with MCF-7. This strongly suggests that MRPL13 could be a potential biomarker for the primary BC and metastatic BC.

Regardless of the fact that our in vitro study failed to show the TCEB1, GOLT1B and METTL1 contribution to cell invasion, these three genes might still be good candidate for BC metastasis. Firstly, the TCEB1, also known as Elongin, is a general transcription elongation factor that increases the RNA polymerase II transcription elongation past template-encoded arresting sites, and TCEB1 is an invasion and metastasis promoting gene in prostate cancer cells [26] and Agell et al also found TCEB1 is a potential marker of progression in prostate cancer [27]. Elongin BC complex was also found can prevent degradation of von Hippel-Lindau tumor suppressor gene products [28]. Moreover, the Golgi transport 1B (GOLT1B), also named Vesicle transport protein GOLT1B, could be involved in fusion of ER-derived transport vesicles with the Golgi complex. Han et al found that the expression of GOLT1B was associated with worse prognosis in lung adenocarcinoma [29]. Finally, the Methyltransferase like 1 (METTL1), consists of seven exons and extends over 3.5 kb, is a novel human methyltransferase-like gene with a high degree of phylogenetic conservation [30]. The functional study revealed that METTL1 is inactivated by PKB and RSK in cells [31] and can promote let-7 MicroRNA processing via m7G methylation [32]. Additionally, METTL1 overexpression was also found be correlated with poor prognosis and promotes hepatocellular carcinoma via PTEN [33].

Despite sufficient powerful mastery and analysis, one of the limitations of our study might be we only knockdown the expression of MRPL13 in vitro, which does not allow definite conclusion for TCEB1, GOLT1B and METTL1. Another limitation is no date in vivo supports our results. Further experiments on mouse model and the specific signaling pathways upstream and downstream are needed.

Nevertheless, this study also has several strengths, including combination of multi-database and experiments validation, ten hub genes were identified and confirmed, three were shown be associated with the worse over survival, and MRPL13 was verified be a metastatic and prognostic biomarker. This all could be useful in guiding future research into mechanisms of breast cancer metastasis process, providing candidate therapeutic targets and biomarkers for breast cancer.

In summary, this study presented a comprehensive bioinformatics analysis of differentially expressed genes (DEGs) in breast cancer, which might contribute to the research of tumorigenesis or progression in breast cancer patients. Furthermore, MRPL13 has been identified and verified experimentally as a metastatic and prognostic biomarker. Study in vivo and the specific upstream and downstream signaling pathways are further needed.

BC, breast cancer; GEO, Gene Expression Omnibus; DEGs, differentially expressed genes; GO, gene ontology; FBS, fetal bovine serum; PBS, phosphate-buffered saline; KEGG, Kyoto Encyclopedia of Genes Genomes; PPI, protein-protein interaction; TCGA, The Cancer Genome Atlas; OS, overall survival; IHC, Immunohistochemistry; SD, standard deviation; MRPL13, mosomal protein L13; METTL1, Methyltransferase like 1

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Review Board of the Hunan Provincial People's Hospital (Number 078). Informed consent was obtained from all subjects involved in the study.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request. The publicly published TCGA (https://www.cancer.gov/ccg/research/genome-sequencing/tcga) and GEO datasets (http://www.ncbi.nlm.nih.gov/geo) are available online.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Fundings

This work was supported by the Hunan Provincial People's Hospital Doctoral Fund project ( BSJJ202203 to P.D and BSJJ202211 to ZB.C).

Authors' contributions

Pei Dai and Yan’an Chen conceived and designed the study. Xiao Zhang evaluated the IHC staining and score, Pei Dai collected the data, Yan’an Chen managed the data base, analysed the data. Long Liu contributed in the interpretation of the data. Pei Dai, Yan’an Chen and Xiao Zhang performed the experiments. Zhenbo Cheng drafted the article and revised it critically for important intellectual content. All authors have approved the final version of the manuscript to be published.

Acknowledgments

The authors are grateful to all the participants in this study. Thanks for the language editing and proofreading from Editage.

Harbeck N, Gnant M: Breast cancer. The Lancet 2017, 389(10074):1134-1150.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians 2021, 71(3):209-249.
Rostami R, Mittal S, Rostami P, Tavassoli F, Jabbari B: Brain metastasis in breast cancer: a comprehensive literature review. Journal of neuro-oncology 2016, 127(3):407-414.
Brogi E, Murphy CG, Johnson ML, Conlin AK, Hsu M, Patil S, Akram M, Nehhozina T, Jhaveri KL, Hudis CA et al: Breast carcinoma with brain metastases: clinical analysis and immunoprofile on tissue microarrays. Annals of oncology : official journal of the European Society for Medical Oncology 2011, 22(12):2597-2603.
Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B, Varambally S: UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia (New York, NY) 2017, 19(8):649-658.
Liu Y, Sun H, Li X, Liu Q, Zhao Y, Li L, Xu B, Hou Y, Jin W: Identification of a Three-RNA Binding Proteins (RBPs) Signature Predicting Prognosis for Breast Cancer. Frontiers in oncology 2021, 11:663556.
Tikhonov A, Smoldovskaya O, Feyzkhanova G, Kushlinskii N, Rubina A: Glycan-specific antibodies as potential cancer biomarkers: a focus on microarray applications. Clinical chemistry and laboratory medicine 2020.
Ang L, Guo L, Wang J, Huang J, Lou X, Zhao M: Oncolytic virotherapy armed with an engineered interfering lncRNA exhibits antitumor activity by blocking the epithelial mesenchymal transition in triple-negative breast cancer. Cancer letters 2020, 479:42-53.
Wu JR, Zhao Y, Zhou XP, Qin X: Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 2020, 121:109647.
Lee JY, Park K, Lee E, Ahn T, Jung HH, Lim SH, Hong M, Do IG, Cho EY, Kim DH et al: Gene Expression Profiling of Breast Cancer Brain Metastasis. Sci Rep 2016, 6:28623.
Gautier L, Cope L, Bolstad BM, Irizarry RA: affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics (Oxford, England) 2004, 20(3):307-315.
Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology 2004, 3:Article3.
Pathan M, Keerthikumar S, Chisanga D, Alessandro R, Ang CS, Askenase P, Batagov AO, Benito-Martin A, Camussi G, Clayton A et al: A novel community driven software for functional enrichment analysis of extracellular vesicles data. Journal of extracellular vesicles 2017, 6(1):1321455.
Dalby B, Cates S, Harris A, Ohki EC, Tilkins ML, Price PJ, Ciccarone VC: Advanced transfection with Lipofectamine 2000 reagent: primary neurons, siRNA, and high-throughput applications. Methods 2004, 33(2):95-103.
Wang Y, Zhai W, Wang H, Xia X, Zhang C: Benzo(a)pyrene promotes A549 cell migration and invasion through up-regulating Twist. Archives of Toxicology 2014, 89(3):451-458.
Wolff AC, Hammond MEH, Allison KH, Harvey BE, Mangu PB, Bartlett JMS, Bilous M, Ellis IO, Fitzgibbons P, Hanna W et al: Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2018, 36(20):2105-2122.
Chan YL, Olvera J, Glück A, Wool IG: A leucine zipper-like motif and a basic region-leucine zipper-like element in rat ribosomal protein L13a. Identification of the tum- transplantation antigen P198. The Journal of biological chemistry 1994, 269(8):5589-5594.
Grohmann L, Kitakawa M, Isono K, Goldschmidt-Reisin S, Graack HR: The yeast nuclear gene MRP-L13 codes for a protein of the large subunit of the mitochondrial ribosome. Current genetics 1994, 26(1):8-14.
Gruschke S, Gröne K, Heublein M, Hölz S, Israel L, Imhof A, Herrmann JM, Ott M: Proteins at the polypeptide tunnel exit of the yeast mitochondrial ribosome. The Journal of biological chemistry 2010, 285(25):19022-19028.
Lee YK, Lim JJ, Jeoun UW, Min S, Lee EB, Kwon SM, Lee C, Yoon G: Lactate-mediated mitoribosomal defects impair mitochondrial oxidative phosphorylation and promote hepatoma cell invasiveness. The Journal of biological chemistry 2017, 292(49):20208-20217.
Ke H, Dass S, Morrisey JM, Mather MW, Vaidya AB: The mitochondrial ribosomal protein L13 is critical for the structural and functional integrity of the mitochondrion in Plasmodium falciparum. The Journal of biological chemistry 2018, 293(21):8128-8137.
Mukhopadhyay R, Ray PS, Arif A, Brady AK, Kinter M, Fox PL: DAPK-ZIPK-L13a axis constitutes a negative-feedback module regulating inflammatory gene expression. Molecular cell 2008, 32(3):371-382.
Wang K, Li L, Fu L, Yuan Y, Dai H, Zhu T, Zhou Y, Yuan F: Integrated Bioinformatics Analysis the Function of RNA Binding Proteins (RBPs) and Their Prognostic Value in Breast Cancer. Frontiers in pharmacology 2019, 10:140.
Xu YH, Deng JL, Wang LP, Zhang HB, Tang L, Huang Y, Tang J, Wang SM, Wang G: Identification of Candidate Genes Associated with Breast Cancer Prognosis. DNA and cell biology 2020, 39(7):1205-1227.
Cai M, Li H, Chen R, Zhou X: MRPL13 Promotes Tumor Cell Proliferation, Migration and EMT Process in Breast Cancer Through the PI3K-AKT-mTOR Pathway. Cancer management and research 2021, 13:2009-2024.
Jalava SE, Porkka KP, Rauhala HE, Isotalo J, Tammela TL, Visakorpi T: TCEB1 promotes invasion of prostate cancer cells. International journal of cancer 2009, 124(1):95-102.
Agell L, Hernández S, Nonell L, Lorenzo M, Puigdecanet E, de Muga S, Juanpere N, Bermudo R, Fernández PL, Lorente JA et al: A 12-gene expression signature is associated with aggressive histological in prostate cancer: SEC14L1 and TCEB1 genes are potential markers of progression. The American journal of pathology 2012, 181(5):1585-1594.
Schoenfeld AR, Davidowitz EJ, Burk RD: Elongin BC complex prevents degradation of von Hippel-Lindau tumor suppressor gene products. Proceedings of the National Academy of Sciences of the United States of America 2000, 97(15):8507-8512.
Han X, Tan Q, Yang S, Li J, Xu J, Hao X, Hu X, Xing P, Liu Y, Lin L et al: Comprehensive Profiling of Gene Copy Number Alterations Predicts Patient Prognosis in Resected Stages I-III Lung Adenocarcinoma. Frontiers in oncology 2019, 9:556.
Bahr A, Hankeln T, Fiedler T, Hegemann J, Schmidt ER: Molecular analysis of METTL1, a novel human methyltransferase-like gene with a high degree of phylogenetic conservation. Genomics 1999, 57(3):424-428.
Cartlidge RA, Knebel A, Peggie M, Alexandrov A, Phizicky EM, Cohen P: The tRNA methylase METTL1 is phosphorylated and inactivated by PKB and RSK in vitro and in cells. The EMBO journal 2005, 24(9):1696-1705.
Pandolfini L, Barbieri I, Bannister AJ, Hendrick A, Andrews B, Webster N, Murat P, Mach P, Brandi R, Robson SC et al: METTL1 Promotes let-7 MicroRNA Processing via m7G Methylation. Molecular cell 2019, 74(6):1278-1290.e1279.
Tian QH, Zhang MF, Zeng JS, Luo RG, Wen Y, Chen J, Gan LG, Xiong JP: METTL1 overexpression is correlated with poor prognosis and promotes hepatocellular carcinoma via PTEN. Journal of molecular medicine (Berlin, Germany) 2019, 97(11):1535-1545.

No competing interests reported.

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MRPL13 is a metastatic and prognostic marker of breast cancer: a silico analysis accompanied with experimental validation

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Abstract

Figures

Introduction

Materials and methods

Ethics approval statement

Study design

Samples collection and processing

Bioinformatic data source and analysis

Cell culture and treatments

Western blot assay

Wound healing scratch assay

Transwell invasion assay

Immunohistochemistry (IHC)

Statistical analysis

Results

Identification of differentially expressed genes (DEGs) between primary and metastases breast cancer

Functional enrichment analysis of differentially expressed genes (DEGs)

Validation of the hub genes

Prognostic significance analysis

Wound healing ability of cell lines

Invasion of MDA-MB-231 and MCF7 with MRPL13 knockdown

Immunohistochemical for MRPL13 in clinic breast cancer tissues

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

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